Luke S. Snyder

TL;DR: A novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements.

TL;DR: This work introduces a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to the current pandemic and for future health emergencies.

TL;DR: MetricsVis as discussed by the authors is a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations, and provides four primary visual components to expedite performance evaluation: a priority adjustment view to support direct manipulation on evaluation metrics; a reorderable performance matrix to demonstrate the details of individual employees; a group performance view that highlights aggregate performance and individual contributions for each group; and a projection view illustrating employees with similar specialties to facilitate shift assignments and training.

TL;DR: This paper adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness.

TL;DR: The opportunities and barriers for the effective use of social media data from first responders' perspective are explored and the impact of SMART—a social media visual analytics system—on first responder operations is assessed.